Detection of Recombinant Proteins SOX2 and OCT4 Interacting in HEK293T Cells Using Real-Time Quantitative PCR.
Darkhan KanayevDiana AbilmazhenovaIlyas AkhmetollayevAliya SekenovaVyacheslav OgayArman KulyyassovPublished in: Life (Basel, Switzerland) (2022)
In vivo biotinylation using wild-type and mutants of biotin ligases is now widely applied for the study of cellular proteomes. The commercial availability of kits for the highly efficient purification of biotinylated proteins and their excellent compatibility with LC-MS/MS protocols are the main reasons for the choice of biotin ligases. Since they are all enzymes, however, just a very low expression in cells is required for experiments. Therefore, it can be difficult to perform the quantifications of these enzymes in various samples. Traditional methods, such as western blotting, are not always fit for the detection of the expression levels. Therefore, real-time qRT-PCR, a technology that is more sensitive, was used in this study to quantify the expression of BirA fusions. Using this method, we detected high expression levels of BirA fusions in models of interactions of pluripotency transcription factors to carry out their relative quantification. We also found the absence of the competing endogenous proteins SOX2 and OCT4, as well as no cross-reactivity between BAP/BirA and the endogenous biotinylation system in HEK293T cells. Thus, these data indicated that the high level of biotinylation is due to the in vivo interaction of BAP-X and BirA-Y (X,Y = SOX2, OCT4) in the cell rather than their random collision, a big difference in the expression level of BirA fusions across samples or endogenous biotinylation.
Keyphrases
- poor prognosis
- transcription factor
- highly efficient
- stem cells
- binding protein
- optical coherence tomography
- long non coding rna
- wild type
- single cell
- diabetic retinopathy
- induced apoptosis
- big data
- south africa
- machine learning
- electronic health record
- mesenchymal stem cells
- cell proliferation
- deep learning
- artificial intelligence
- mass spectrometry
- embryonic stem cells